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Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you... show more

LlamaIndex 🦙

112,786 subscribers

475,732 görüntüleme • 2 yıl önce •via X (Twitter)

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Ian Maurer 🧬🤖🐍 profil fotoğrafı

Ian Maurer 🧬🤖🐍2 yıl önce

@streamlit @OpenAI Like @simonw specified in a recent blog article I had trouble getting Gpts to read their uploaded knowledge. Can’t wait to try this.

Kent Langley profil fotoğrafı

Kent Langley2 yıl önce

Well, this is really cool! I was able to replicate one of my personal GPT's quickly and easily after a little fussiness on the install (my env. not really the install). Using a venv makes it go much better! In some areas this exceeded the native GPT implementation due to tuning parameters I think.

Just Kris profil fotoğrafı

Just Kris2 yıl önce

@streamlit @OpenAI @hive_echo

Pavel Kosyrev profil fotoğrafı

Pavel Kosyrev2 yıl önce

It’s an awesome tool May I suggest, there is something that I struggle with and I think a lot people would appreciate this If I could explain my use case and I could chat with an assistant to choose the best retrieval method, index, synthesiser etc. There are so many moving parts in llame index and sometimes it gets overwhelming, docs are good, the chat with docs is useless I would rather know how to dial the code than have it automatically set up a RAG For GPT type of thins with RAG there are many resources, drip you files and it does some magic But there are not many libraries like yours to build RAG manually

sujit kumar ojha profil fotoğrafı

sujit kumar ojha2 yıl önce

@streamlit @OpenAI @memdotai Mem it

Mem profil fotoğrafı

Mem2 yıl önce

@llama_index @streamlit @OpenAI Saved! Here's the compiled thread: 🪄 AI-generated summary: "RAGs is a Streamlit app that allows users to create and customize their own RAG agent and use it to search and retrieve data using natural language. It is inspired by OpenAI...

Dani Acosta profil fotoğrafı

Dani Acosta2 yıl önce

@streamlit @OpenAI You are rockstars! Well done

The Content profil fotoğrafı

The Content2 yıl önce

@streamlit @OpenAI @memdotai mem it

Mem profil fotoğrafı

Mem2 yıl önce

@llama_index @streamlit @OpenAI Saved! Here's the compiled thread: 🪄 AI-generated summary: "RAGs is a Streamlit app that allows users to create and customize their own RAG agent and use it to search and retrieve data using natural language. It is inspired by OpenAI...

Bryce Amacker profil fotoğrafı

Bryce Amacker2 yıl önce

@streamlit @OpenAI @memdotai mem it

Mem profil fotoğrafı

Mem2 yıl önce

@llama_index @streamlit @OpenAI Saved! Here's the compiled thread: 🪄 AI-generated summary: "RAGs is a Streamlit app that allows users to create and customize their own RAG agent and use it to search and retrieve data using natural language. It is inspired by OpenAI...

Benzer Videolar

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

Andrew Ng

218,703 görüntüleme • 2 yıl önce

We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬 A huge upgrade vs. the initial launch: 💫 Easily create multiple RAG pipelines and save them 💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) 💫 Delete unused RAG pipelines 💫 (dev quality) added much-needed linting/CI Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features: 🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline 🌐 Supports loading load files or web pages. Check out our repo here:

We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬 A huge upgrade vs. the initial launch: 💫 Easily create multiple RAG pipelines and save them 💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) 💫 Delete unused RAG pipelines 💫 (dev quality) added much-needed linting/CI Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features: 🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline 🌐 Supports loading load files or web pages. Check out our repo here:

LlamaIndex 🦙

124,202 görüntüleme • 2 yıl önce

Introducing RAG CLI 🧑‍💻🔎 - a dead-simple command-line tool that allows you to RAG literally any file on your local machine. Index any files including glob patterns, such as `$ llamaindex-cli rag --files "./docs/**/*.rst”` To search simply do `$ llamaindex-cli rag --question "What is LlamaIndex?”` Built with our IngestionPipeline and QueryPipeline abstractions that allow efficient, performant data indexing and retrieval. Also built with Chroma. That’s not all! 💡Want to use local models like ollama? 💡Want to bake in advanced ingestion and retrieval techniques? The guide also shows you how to customize the logic for your own needs! Blog post: Docs:

Introducing RAG CLI 🧑‍💻🔎 - a dead-simple command-line tool that allows you to RAG literally any file on your local machine. Index any files including glob patterns, such as `$ llamaindex-cli rag --files "./docs/**/*.rst”` To search simply do `$ llamaindex-cli rag --question "What is LlamaIndex?”` Built with our IngestionPipeline and QueryPipeline abstractions that allow efficient, performant data indexing and retrieval. Also built with Chroma. That’s not all! 💡Want to use local models like ollama? 💡Want to bake in advanced ingestion and retrieval techniques? The guide also shows you how to customize the logic for your own needs! Blog post: Docs:

LlamaIndex 🦙

67,888 görüntüleme • 2 yıl önce

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:

Jerry Liu

55,620 görüntüleme • 9 ay önce

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:

Introducing RAGs v3 🌟: Build a RAG bot that can also search the web 🌐, to find answers that aren’t immediately in its corpus. Do this all in natural language, not code! 💬 Get a better experience than ChatGPT + Bing ⚡️ We used our integration with Metaphor - a search engine designed for LLMs - to pull in relevant text from the internet. Additionally: - you can now view the tools that the agent is using. - note: web search is only accessible for our OpenAI agent Check out the video below, and get started with the repo today. Repo:

LlamaIndex 🦙

76,371 görüntüleme • 2 yıl önce

Introducing LangSmith Agent Builder 🤖🧱 A true agent building experience (not workflows!!), all through a simple natural language interface. Describe your agent to "build" it, then interact with it via chat, or add an automatic trigger. Connect your agents to any MCP server, or use our builtin tools. These agents also come with builtin memory, so they can optimize themselves over time, both ambiently, and from user feedback! Sign up for the waitlist today:

Introducing LangSmith Agent Builder 🤖🧱 A true agent building experience (not workflows!!), all through a simple natural language interface. Describe your agent to "build" it, then interact with it via chat, or add an automatic trigger. Connect your agents to any MCP server, or use our builtin tools. These agents also come with builtin memory, so they can optimize themselves over time, both ambiently, and from user feedback! Sign up for the waitlist today:

Brace

30,846 görüntüleme • 11 ay önce

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

Jerry Liu

21,073 görüntüleme • 8 ay önce

Google Senior Engineer Annie Wang just dropped a free hands-on course on Graph RAG agents, from scratch: Setup → Graph Schema → Embeddings → Hybrid Search (RRF) → ADK Agent → Deploy 0% → 0:00 – a Graph RAG agent with ADK 25% → 13:01 – Build the graph schema and visualize your data 77% → 39:16 – Hybrid search + RRF 84% → 43:05 – Wiring up the ADK agent: runner, session service, memory service 100% → 51:06 – Final demo and deploying the agent with FastAPI This is one of the few guides that walks through real code and architecture, from an empty project to a working agent. Watch it, then build it yourself

Google Senior Engineer Annie Wang just dropped a free hands-on course on Graph RAG agents, from scratch: Setup → Graph Schema → Embeddings → Hybrid Search (RRF) → ADK Agent → Deploy 0% → 0:00 – a Graph RAG agent with ADK 25% → 13:01 – Build the graph schema and visualize your data 77% → 39:16 – Hybrid search + RRF 84% → 43:05 – Wiring up the ADK agent: runner, session service, memory service 100% → 51:06 – Final demo and deploying the agent with FastAPI This is one of the few guides that walks through real code and architecture, from an empty project to a working agent. Watch it, then build it yourself

Morty

19,059 görüntüleme • 21 gün önce

Our new RAG CLI tool lets you RAG any file on your filesystem, using only on-device LLMs/embeddings! 💾🔎 Using LlamaIndex 🦙, Mistral-7B on ollama, and bge-m3, you have access to an LLM-powered grep that you can easily install, use, and customize. Can input any files including glob strings. LlamaPack: We also have the recipe for you to build your own RAG CLI from scratch. Check out this excellent guide by Sourabh Desai:

Our new RAG CLI tool lets you RAG any file on your filesystem, using only on-device LLMs/embeddings! 💾🔎 Using LlamaIndex 🦙, Mistral-7B on ollama, and bge-m3, you have access to an LLM-powered grep that you can easily install, use, and customize. Can input any files including glob strings. LlamaPack: We also have the recipe for you to build your own RAG CLI from scratch. Check out this excellent guide by Sourabh Desai:

LlamaIndex 🦙

40,124 görüntüleme • 2 yıl önce

New short course: Building Your Own Database Agent, created with Microsoft Microsoft Azure's Adrián González Sánchez (He/Him/His). You'll learn to build an AI assistant that translates natural language questions into SQL queries. Querying with natural language empowers everyone in your organization, from business leaders to developers, to access data insights directly, using plain English. You’ll use the Azure OpenAI Service and LangChain to implement retrieval augmented generation and function calling, and leverage the Assistants API. You'll gain hands-on experience building an agent that can reason over both CSV files and SQL databases. Please sign up here!

New short course: Building Your Own Database Agent, created with Microsoft Microsoft Azure's Adrián González Sánchez (He/Him/His). You'll learn to build an AI assistant that translates natural language questions into SQL queries. Querying with natural language empowers everyone in your organization, from business leaders to developers, to access data insights directly, using plain English. You’ll use the Azure OpenAI Service and LangChain to implement retrieval augmented generation and function calling, and leverage the Assistants API. You'll gain hands-on experience building an agent that can reason over both CSV files and SQL databases. Please sign up here!

Andrew Ng

109,951 görüntüleme • 2 yıl önce

🔥 🤖 🕵️‍♂️ INTRODUCING: Meta Agent 💥 this is by far the most exciting thing I've created using AI!!! >>> made using Manus and @windsurf 🔑 Meta Agent is an AI agent that uses the OpenAI Agents SDK to GENERATE NEW AGENTS you simply tell Meta Agent in natural language the agent you want... 🤯 and Meta Agent generates it for you fully open --> link to GitHub and example walkthrough below ⬇️

🔥 🤖 🕵️‍♂️ INTRODUCING: Meta Agent 💥 this is by far the most exciting thing I've created using AI!!! >>> made using Manus and @windsurf 🔑 Meta Agent is an AI agent that uses the OpenAI Agents SDK to GENERATE NEW AGENTS you simply tell Meta Agent in natural language the agent you want... 🤯 and Meta Agent generates it for you fully open --> link to GitHub and example walkthrough below ⬇️

Dan McAteer

153,982 görüntüleme • 1 yıl önce

Wow! Someone open sourced 100+ full AI apps you can just clone and ship.. and it's sitting at 124,000 stars on github it's one repo, awesome-llm-apps. and the important part.. these aren't tutorials teaching you to build an app. they're the finished apps themselves. RAG systems, AI agents, multi-agent teams.. tested end to end, apache licensed, yours to copy. a few that are actually in there: - a fraud investigation agent that cross-references public records to flag things that don't add up - a VC due-diligence agent team that writes startup investment analysis - a financial coach that builds you a budget, debt and savings plan - "chat with anything".. your github, gmail, PDFs, arxiv papers, youtube videos - a deep research agent that runs full web research on its own here's how to actually use it, not just star it: clone the repo, open the one app closest to what you need it's a working template.. swap in your data, your API key, your use case ship it as your own thing, or bolt three of them together into one bigger product the license literally lets you take these, customize them, and sell them. that's not a loophole, that's the point of apache-2.0. repo:

Wow! Someone open sourced 100+ full AI apps you can just clone and ship.. and it's sitting at 124,000 stars on github it's one repo, awesome-llm-apps. and the important part.. these aren't tutorials teaching you to build an app. they're the finished apps themselves. RAG systems, AI agents, multi-agent teams.. tested end to end, apache licensed, yours to copy. a few that are actually in there: - a fraud investigation agent that cross-references public records to flag things that don't add up - a VC due-diligence agent team that writes startup investment analysis - a financial coach that builds you a budget, debt and savings plan - "chat with anything".. your github, gmail, PDFs, arxiv papers, youtube videos - a deep research agent that runs full web research on its own here's how to actually use it, not just star it: clone the repo, open the one app closest to what you need it's a working template.. swap in your data, your API key, your use case ship it as your own thing, or bolt three of them together into one bigger product the license literally lets you take these, customize them, and sell them. that's not a loophole, that's the point of apache-2.0. repo:

Axel Bitblaze 🪓

46,487 görüntüleme • 2 ay önce

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

Jerry Liu

24,245 görüntüleme • 1 yıl önce

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

LangChain

26,513 görüntüleme • 8 ay önce

🐪camelAI is the deep research agent for your structured data, and the last BI tool you will ever need. It connects directly to your database, turning natural language questions into in-depth reports complete with beautiful visualizations in seconds.

🐪camelAI is the deep research agent for your structured data, and the last BI tool you will ever need. It connects directly to your database, turning natural language questions into in-depth reports complete with beautiful visualizations in seconds.

Y Combinator

29,743 görüntüleme • 1 yıl önce

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Jerry Liu

20,434 görüntüleme • 1 yıl önce

🛝🥏🏃‍♀️ Have you checked out the new AI-powered Builder agent on our homepage? Use natural language inputs to create maps for travel, business, special projects and anything else you can think of - limited only by your imagination ➡️

🛝🥏🏃‍♀️ Have you checked out the new AI-powered Builder agent on our homepage? Use natural language inputs to create maps for travel, business, special projects and anything else you can think of - limited only by your imagination ➡️

Google Maps Platform

63,805 görüntüleme • 10 ay önce

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,447 görüntüleme • 11 ay önce